Tray inserts and image quality, systems, methods and algorithms for quantifying tray&#39;s impact using the same

ABSTRACT

Various tray inserts, methods and algorithm for certifying candidate trays for use in an X-ray scanner system are discussed. The tray insert includes at least a body having multiple parts positioned for generation of image quality metrics for tray impact evaluation in; a first cover and a second cover disposed at opposite ends to fix and secure the body. The method including running an algorithm to control an X-ray system to collect baseline image data from certified trays, collecting candidate tray image data, extracting image quality metrics for both the baseline image data and the candidate tray image data, and performing statistical analysis using and comparing image quality metrics from the baseline image data and the candidate tray image data to certify the candidate tray based on the statistical and comparison results.

CROSS REFERENCED TO RELATED APPLICATION

The present application claims the benefit of priority under 35 U.S.C. §120 as a continuation from U.S. patent application Ser. No. 17/365,693entitled “TRAY INSERTS AND IMAGE QUALITY, SYSTEMS, METHODS AND ALGORITHMFOR QUANTIFYING TRAY'S IMPACT USING THE SAME,” filed on Jul. 1, 2021,which claims the benefit of priority under 35 U.S.C. § 119 from U.S.Provisional Patent Application Ser. No. 63/047,639 entitled “TrayInserts”, filed on Jul. 2, 2020, the contents of which are hereinincorporated by reference in their entirety for all purposes.

TECHNICAL FIELD

The present application relates generally to tray inserts, imagequality, systems, methods and algorithms developed for quantifying acandidate tray's impact to the X-ray system's ability to collect qualityimages through the use of certified trays (ECAC certified) by the trayinserts. The method and algorithm may be used to certify unknown or newtrays and provide feedback to trays manufacturer for redesign forcompliance.

BACKGROUND

Computed tomography (CT) and transmission (2D) X-ray systems arecommonly used in the screening for explosive threats for both hold andcabin baggage (CB) in airports around the world. Variants of thesesystems referred to as Explosive Detection Systems (EDS) employautomated explosive detection algorithms that are certified to theEuropean Union (EU) detection standard using the European Civil AviationConference (ECAC) Common Testing Methodology (CTM). Trays or bins areused to transport luggage and divested contents from passengers throughthe systems. Automated security lanes integrate tray return systems tothese X-ray systems, helping increase screening efficiency by allowingsimultaneous divesture of multiple passengers and remote screening capabilities. Recent mandates that require the use of CT for securityscreening as well as increased passenger volume in the flighttransportation have greatly increased the demand for trays at airports.Typically, trays used in these systems pose a challenge for regulatorsand certifying test laboratories because of different variations in traydesigns that may negatively impact X-ray system certified threatdetection performance.

In order to overcome this issue, trays and X-ray system configurationsmust be tested and certified together. This ensures that detectionperformance for a given screening technology can perform at thecertified level for a given tray type. If a tray is not tested with anX-ray system (e.g., an X-ray system applying an EDS/EDS-CB technology),the tray cannot be used with the X-ray systems, thereby limiting trayoptions to airports. Airports typically demand a variety of tray typesto accommodate their typical passenger divesture and hold baggage needs.Tray manufacturers have designed new trays to meet such a demand,thereby increasing the number of combinations of trays and X-raysystems.

Certain tray designs (i.e., physical configurations) and materialsconstructions may produce artifacts causing anomalies or uncertaintiesto the scanned image or impact the image quality in such a way as toaffect the X-ray system's detection performance. This requires each trayand X-ray system combination to be tested and certified. Airportcheckpoint configurations and passenger diversity require flexibility inthe type and size of tray variations. This has produced a surge in newcombinations of trays and X-ray systems, testing laboratories arestruggling to keep up tray certifications. The current implementation ofthe certification test method to certify these trays and X-ray systemcombinations are cumbersome, time consuming, and costly. If the traysare not tested with an X-ray system, the trays cannot be used, limitingtray options to airports.

SUMMARY

The present application discloses tray inserts for certifying trays foruse in an X-ray system, which substantially solve one or more existingtechnical problems due to limitations and disadvantages of the relatedart. The present application also discloses a method and an algorithm toquantify tray's impact to image quality in the X-ray system.

In an example, different types of tray inserts are disclosed. In anexample, a tray insert may include a body, the body having multipleparts removably positioned and prearranged therein for generation ofimage quality metrics for tray impact evaluation in an X-ray system. Thetray insert also includes a first cover disposed at a first end of thebody; and a second cover disposed at a second end of the body, whereinthe first cover and the second cover are configured to fix and securethe body at both ends.

The tray inserts may provide a better solution to support thecertification of trays that can be performed quickly and at reduced costwhile providing confidence that detection performance is not impacted bythe tray. The tray inserts may allow us to quantify impact of a tray onimage quality. Further, there may be an acceptance threshold andstatistical test to objectively determine whether the tray can be usedwith a particular X-ray system without impacting the threat detectionperformance.

In another example, a computer implemented method to quantify X-rayscanner system's image quality impact by a candidate tray is disclosed.The method includes executing by at least a processor in a computer, atleast one code stored in a non-transitory computer-readable medium whichcauses the computer to control an X-ray scanner system to quantifycandidate tray imp act, by performing the following steps: collectingbaseline image data for the X-ray scanner system that includescharacteristic image quality data collected from a plurality ofcertified trays that have previously been scanned with a plurality ofselected tray inserts; collecting one or more candidate trays image datafor the X-ray scanner system that includes characteristic image qualitydata collected from the one or more candidate trays that have beenscanned with a same plurality of selected tray inserts; extracting imagequality metrics for both the baseline image data and the one or morecandidate trays image data; performing statistical analysis using theimage quality metrics within a volume of interest from both the baselineimage data and the candidate trays image data; and reporting orcertifying the one or more candidate tray suitable for use in the X-rayscanner system based on the image quality metrics of the one or morecandidate tray falling within a mean and a standard deviation ofmultiple Image Quality baseline metrics within the volume of interest.

The collecting of the baseline image data or the one or more candidatetrays image data may include taking turns to scan by the X-ray scannersystem, a same selected tray inserts which has been centrally positionedin the certified trays for a defined number of times, afterwardsscanning the candidate trays using the same selected tray inserts, untilall remaining selected tray inserts have been used by the plurality ofcertified trays or the one or more candidate trays for scanning by theX-ray scanner system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram generally illustrating a tray insert according tothe first embodiment of this application;

FIG. 2A is a perspective and assembled view illustrating a tray insertaccording to the second embodiment of this application;

FIG. 2B is an exploded view illustrating the tray insert shown in theFIG. 2A;

FIG. 3A is a perspective and assembled view illustrating a tray insertaccording to the third embodiment of this application;

FIG. 3B is an exploded view illustrating the tray insert shown in theFIG. 3A;

FIG. 4A is a perspective and assembled view illustrating a tray insertaccording to the fourth embodiment of this application;

FIG. 4B is an exploded view illustrating the tray insert shown in theFIG. 4A;

FIG. 5A is a perspective and assembled view illustrating a tray insertaccording to the fifth embodiment of this application;

FIG. 5B is an exploded view illustrating the tray insert shown in theFIG. 5A;

FIG. 6A is a perspective and assembled view illustrating a tray insertaccording to the sixth embodiment of this application;

FIG. 6B is an exploded view illustrating the tray insert shown in theFIG. 6A;

FIG. 7A is a perspective and assembled view illustrating a tray insertaccording to the seventh embodiment of this application;

FIG. 7B is an exploded view illustrating the tray insert shown in theFIG. 7A;

FIG. 8A is a perspective and assembled view illustrating a tray insertaccording to the eighth embodiment of this application;

FIG. 8B is an exploded view illustrating the tray insert shown in theFIG. 8A;

FIG. 9A is a perspective and assembled view illustrating a tray insertaccording to the ninth embodiment of this application;

FIG. 9B is an exploded view illustrating the tray insert shown in theFIG. 9A;

FIG. 10A is a perspective and assembled view illustrating a tray insertaccording to the tenth embodiment of this application;

FIG. 10B is an exploded view illustrating the tray insert shown in theFIG. 10A;

FIG. 11A is a perspective and assembled view illustrating a tray insertaccording to the eleventh embodiment of this application;

FIG. 11B is an exploded view illustrating the tray insert shown in theFIG. 11A;

FIG. 12A is a perspective and assembled view illustrating a tray insertaccording to the twelfth embodiment of this application;

FIG. 12B is an exploded view illustrating the tray insert shown in theFIG. 12A;

FIG. 13A is a perspective and assembled view illustrating a tray insertaccording to the thirteenth embodiment of this application;

FIG. 13B is an exploded view illustrating the tray insert shown in theFIG. 13A;

FIG. 14A is a perspective and assembled view illustrating a tray insertaccording to the fourteenth embodiment of this application;

FIG. 14B is an exploded view illustrating the tray insert shown in theFIG. 14A;

FIG. 15A is a perspective and assembled view illustrating a tray insertaccording to the fifteenth embodiment of this application;

FIG. 15B is an exploded view illustrating the tray insert shown in theFIG. 15A;

FIG. 16A is a perspective and assembled view illustrating a tray insertaccording to the sixteenth embodiment of this application;

FIG. 16B is an exploded view illustrating the tray insert shown in theFIG. 16A;

FIGS. 17A and 17B illustrate a tray insert used with a tray.

FIG. 18 is an overall flowchart depicting a method and an algorithm forcertifying a tray using image quality (IQ) data of already certifiedtray by an X-ray system.

FIGS. 19A-19F illustrate examples of X-ray artifacts impacted by thetray design of after image data reconstruction

FIGS. 20A-20B illustrate an example of metrics extraction process tocircumvent certain tray design impacts.

DETAILED DESCRIPTION

Automated threat detection (ATD) algorithms are certified to meetestablished regulatory threats detection requirements. These algorithmsare dependent on image quality (IQ) produced by X-ray systems.Degradation of the image can occur due to defects in hardware componentsin the X-ray systems and even the improper setup of the system duringinstallation. Therefore, it is critical that an X-ray system is setupand configured properly and that all hardware components are operatingin a nominal state to support certified threats detection performance.

The image quality may be impacted by an imaging subsystem of X-rayscreening technology (e.g., X-ray tube, power supply, belt motor). Suchan imaging subsystem is unique to each platform that embodies thattechnology because each X-ray source, reconstruction algorithm, X-raydetector(s), conveyor belt speed, and other components may vary betweenmanufacturers.

Also, the image quality may be impacted by a tray used together with theX-ray system. That is, different types of trays may have differentimpacts to the image quality. This application is directed to apotential impact a tray may have on image quality and detectionperformance. In order to make it easier, faster and more cost-efficientto verify/test different trays from different manufacturers throughX-ray systems, this application discloses multiple Image Quality (IQ)tray inserts (i.e., phantoms) which may be used to assess and baselinemultiple X-ray system IQ metrics. In other words, through the trayinserts, the problem regarding tray and X-ray system verificationdescribed above may be solved. The following description will describethe tray inserts disclosed in this application.

Generally speaking, at least one of the tray inserts (i.e., phantoms)disclosed in this application may be placed in or attached to a tray toassess any impact that the tray may have on an X-ray system's ability todetect threats. In this application, unless otherwise indicated, theterms “test tray insert”, “tray insert”, “insert” and “phantom” may beused interchangeably. It should be noted that in order to certify trays,besides the tray inserts, there may be the following devices needed: anX-ray device and a 3rd party computing device.

The X-ray device may be used to collect image data, e.g., images of aphantom within a candidate tray. The 3rd part computing device may beused to perform data analysis (e.g., extracting and analyzing the imagequality metrics from the image data), data comparison (e.g., comparingthe image quality metrics with baseline metrics). The 3^(rd) partycomputing device may be a computer, a laptop, a smart phone, or anyother kind device which may be qualified to perform its functionsdisclosed in this application. Under some circumstances, the 3^(rd)party computing device and the X-ray device may be incorporated into acomprehensive system. In some cases, the 3^(rd) party computing devicemay be a part of the X-ray device. The present application does notlimit the relationship between the X-ray device and the 3rd partycomputing device, as long as these two devices are consistent with theprinciples taught by this application. It should be recognized that theX-ray device is one of the main components in the X-ray system which mayfurther include power supply device, conveyor belt, etc. Here in thisapplication, unless otherwise indicated, the terms “X-ray system” and“X-ray device” may be used interchangeably. Also, unless otherwiseindicated, the terms “3rd party computing device” and “computing device”may be used interchangeably.

The tray inserts disclosed in this application may be developed ordesigned based on ANSI/IEC standards, such as ANSI N42.45. The contentof ANSI N42.45 is incorporated into this application by reference.Although this application incorporates standard ANSI N42.45, thatstandard is not intended to be exclusive or be limiting to the presentapplication. Any other available standard related to an X-ray system andits associated tray may be applied to design a tray insert according tothe principles disclosed in this application.

The tray inserts will be designed with a low profile so that they may beused to test as many types of trays as possible. In embodiments, thetray inserts may have a smaller 3D size than those trays to be tested.For example, the length, width and height of the tray inserts may besmaller than those of the trays to be tested. It should be noted thatthe above exemplary size of the tray inserts is not intended to beexclusive or be limiting to the present application. The 3D size of thetray inserts may vary as long as they may realize their functionsdisclosed in this application.

The tray inserts may be used to evaluate image quality metrics thatcould potentially be impacted by a tray and thereby alter the X-raysystem's certified detection performance. For example, the image qualitymetrics may include at least one of the following: object lengthaccuracy, CT value consistency, path length CT value and Z_(eff), NoiseEquivalent Quanta (NEQ), etc. Generally, the image quality metrics(e.g., metrics value/metrics data) will be obtained from the trayinserts and will be analyzed by the 3rd party computing device (e.g., acomputer). For example, through the 3rd party computing device running aspecifically designed algorithm, the image quality metrics (e.g.,metrics data) may be compared with a baseline metrics (e.g., baselinemetrics data) so as to determine whether there is a negative effect fromthe tray. Also, as well known, an X-ray system may be used to detectthreats if the 3^(rd) party computing device is also running a threatdetection algorithm to analyze image data collected by the X-ray system.Therefore, under that circumstance, the image quality metrics disclosedhere in this application may also be related to threat detection. Inother words, the image quality metrics may be used to certify traysthrough the tray inserts disclosed in this application on the one hand,and on the other hand, they might also be used to assess impacts onthreat detection. Here in this application, unless otherwise indicated,the terms “image quality metrics” and “metrics” may be usedinterchangeably.

It should be appreciated that the above-mentioned exemplary metrics arenot intended to be exclusive or be limiting to the metrics adopted bythis application to certify trays. Any metrics may be available as longas they may be used by the 3^(rd) party computing device for the purposeof certifying trays. The following description will describe the metricsand with reference to detailed examples.

The basic idea of the use of the image quality metrics to certify traysfor use with X-ray systems is briefly described as follows. First, a setof hardware phantoms (i.e., tray inserts) are specifically designed tofit within trays. Then, an algorithm (e.g., image quality algorithm) maybe developed based on the solution that has been used for verificationimage quality (VIQ) acceptance testing solution on the market. Then, astatistical test may be performed by the computing device through thealgorithm. For example, the computing device may performanalysis/comparison on the obtained image quality metrics by comparingthose image quality metrics with the baseline metrics.

Typically, for a given X-ray system, its metrics would be approximatelyconsistent because its image collection ability is fixed whenmanufactured. For example, an object length accuracy of an X-ray systemmay be calculated using the following equation (1).

$\begin{matrix}{{{object}{length}{accuracy}{value}} = \frac{length}{{length}_{physical}}} & (1)\end{matrix}$

Here, length represents an object's length detected by the X-ray system,while length_(physical) represents a physical length of the object. Itwill be appreciated that unless otherwise indicated, the terms “objectlength accuracy value” and “object length accuracy” may be usedinterchangeably. Similarly, when a metrics is discussed in thisapplication, it may be directed to an Image Quality metrics value.

The X-ray system may scan a tray insert in a tray and obtain image data.Then the computing device may calculate the image data and obtain a newobject length accuracy based on the tray insert in the tray. Thecomputing device may compare the new object length accuracy value withits original object length accuracy value, and then determine whetherthere is an effect from the tray.

The computing device may use a metric's condition to make thedetermination. That is, if the metric's condition is satisfied by thecomparison between a new metric's (e.g., the new object length accuracydiscussed above) and an original metric's (e.g., the baseline metricsdiscussed above), then the computing device may determine that an effectfrom the tray exists. The condition may be a threshold value, athreshold difference value, etc.

In an embodiment, the condition is a threshold value for the objectlength accuracy. For example, in a scenario, the threshold value is 70%,while the new object length accuracy (i.e., using a tray) is 80%,greater than the threshold value (i.e., the condition is satisfied).Here, the computing device may determine that there is an effect to theimage quality metric (i.e., object length accuracy) from the tray. Ifthe image quality metric is also used for threat detection, because ofthe effect from the tray, the threat detection performance may beimpacted too.

In an embodiment, there may be a threshold difference value for theobject length accuracy. If the difference between a new metric (e.g.,the new object length accuracy discussed above) and an original metric(e.g., the original object length accuracy discussed above) is greaterthan or equal to the threshold difference value, then the computingdevice may determine that the tray negatively imp acts the accuracy ofdetection performance (either image detection or threat detection, orboth). If the difference is smaller than the threshold value, the traymay be used with this X-ray system. For example, the thresholddifference value is 5%. An X-ray system's original object lengthaccuracy (i.e., without using any tray) is 90%, while its new objectlength accuracy (i.e., using a tray) is 80%. Then, the difference is10%, greater than the threshold difference value, and thus the computingdevice may determine that an effect from the tray exists.

It should be noted that the above example of threshold value is onlygiven by way of example, and it's not intended to be limiting to thepresent application. Also, the threshold value is only one of availableways for a computing device to determine an effect from a tray.Therefore, any other available ways may be used by the computing deviceto make such a determination.

The above description describes how to make the determination using aspecific metric, i.e., the object length accuracy. It will beappreciated that the same or similar idea/principle may also be appliedto the metrics disclosed in the ANSI N42.45 Image Quality standard otherthan the object length accuracy. For example, the above-discusseddetermination regarding the effect from the tray may also be performedbased on CT value consistency, Noise Equivalent Quanta (NEQ), etc.

It should be noted that such a determination may not be based on asingle metric, and it may be a determination based on a combination ofmultiple image quality metrics. In one embodiment, the computing devicemay determine that an effect from the tray exists if all metrics in thecombination of image quality metrics are impacted by the tray. Inanother embodiment, the computing device may determine that an effectfrom the tray exists if at least one metrics in the combination of imagequality metrics are impacted by the tray.

For example, the determination may be based on both the object lengthaccuracy and the CT value consistency. That is, the computing devicewill determine that an effect from the tray exists based on both objectlength accuracy and the CT value consistency, and only if each of thetwo metrics shows a negative effect from the tray, it will determinethat there is such an effect from the tray. For example, there may be afirst threshold value for the object length accuracy and a secondthreshold value for the CT value consistency. If a newly obtained objectlength accuracy using a tray carrying a tray insert is greater than orequal to the first threshold value and a newly obtained CT valueconsistency using the tray carrying the tray insert is greater than orequal to the second threshold value, then the computing device maydetermine that there is an effect from the tray.

Further, as security check personnel may have to assess on-screen imagesof baggage contents for other threat types (e.g. guns and knives), thetray inserts may include test objects to evaluate potential impacts toon-screen image quality performance. For example, the tray inserts maybe used to evaluate image quality metrics that may be imp acted by atray and thereby alter the on-screen image quality performance. Further,a threat detection algorithm may be used to process the image qualitymetrics so as to do threat detection. Therefore, it may be seen thatusing the image quality metrics to evaluate effects that trays may haveon threat detection makes this a unique application of image qualitymetrics.

Here may be a proposed approach to how the tray inserts may be used fortray certification. ECAC has certified explosives detection performancefor a number of EDS and tray combinations. For example, EDS-A withTray-A, Tray-B, and Tray-C. The use of these three trays is approved andcertified by ECAC on system EDS-A. However, trays Tray-D and Tray-F havenot been certified due to detection performance issues.

For Tray-A, Tray-B, and Tray-C, acceptable thresholds of the imagequality metrics may be established using one or more tray insertsdisclosed in this application since these trays were already certifiedby ECAC. For Tray-D and Tray-F, unacceptable thresholds may beestablished for these tray variants to create a region of acceptableperformance and unacceptable performance. Once these thresholds havebeen defined, these thresholds may be used to evaluate new tray variantsfor EDS-A. Thus, the use of the tray inserts will greatly accelerate thetesting of new tray variants and will likely not require a fullexplosives detection certification test.

The tray inserts according to different embodiments in the presentapplication will be described below with reference to FIGS. 1-17B,whereas FIGS. 18-21B describe the method, system and algorithm tocertify other trays using image qualities of tray inserts in certifiedtrays.

FIG. 1 generally shows a tray insert 100 according to the firstembodiment. In an example, the tray insert 100 includes a body portion102, the body portion 102 having multiple p arts removably positionedand prearranged therein (see FIGS. 2A to 16B) for generation of imagequality metrics for tray impact evaluation in an X-ray system. The trayinsert 100 also includes a first cover 106 disposed at a first end ofthe body; and a second cover 108 disposed at a second end of the body,wherein the first cover 106 and the second cover 108 are configured tofix and secure the body portion 102 at both ends.

As shown in FIG. 1 , the tray insert 100 may be a cube-like device. Thetray insert 100 may include: a body 102 and covers 106, 108 at each endof the body 102. The body 102 of the tray insert 100 is shown in whitecolor in FIG. 1 . The body 102 may include two parts, as shown in FIG. 1, one on the top 102A and one at the bottom 102B. The covers 106, 108are shown in grayscale color in FIG. 1 . The covers 106, 108 may be usedto fix or to hold in place, the two parts 102A, 102B in the bodytogether. Multiple parts or subparts (not shown in FIG. 1 , but shown inFIGS. 2A-16B) may be incorporated within the body.

As discussed above, the tray insert 100 is designed with a low profile(i.e., relative to the depth of a tray). Here this application does notlimit the size of the tray insert. Basically, the size of the trayinsert may vary based on the tray size dimensions. Also, the colorsshown in FIG. 1 as well as other drawings are only given by way ofillustration, and they are not intended to be limiting to the design ofthe tray inserts. The tray insert 100 may be not transparent as shown inFIG. 1 and thus components (also known as objects, elements or parts)inside the tray insert may not be seen by a user. Alternately, the trayinsert 100 may be transparent (at least the body portion 102) so thatits internal structure may be seen from outside. The multiple parts(i.e., internal structures in FIGS. 2A to 16B), may be configured forscreening presence of explosive threats by the X-ray system thatutilizes screening technology comprising one of: two-dimensional (2-D)X-ray or X-ray computed tomography (CT).

It should be noted that the words shown on the left-end cover of thetray insert may provide some information about the tray insert, such asits manufacturer (e.g., Battelle), its Part number (e.g., 2000503-10),its measured parameters (e.g., material specific effective atomicnumber), etc. Those words on the cover are not intended to be limitingto the tray insert or the present application.

It should also be noted that the 3D appearance/shape of the tray insertshown in FIG. 1 is not intended to be exclusive or be limiting to thetray inserts disclose din this application. Basically, the tray insertsmay have any other available 3D appearance/shape as long as thatappearance/shape may help to realize the function of the tray insertsdisclosed in this application.

The present application discloses additional 15 tray inserts (see FIGS.2A to 16B) in 15 embodiments (each corresponding to one embodiment). Thefollowing Table 1 generally lists some OEM information regarding eachtray insert in these 15 embodiments for illustration only.

TABLE 1 Table of the VIQ Certification Tray Phantom Set Kit Part Number:2000502-10 Phantom Equipment Part Measured No. Phantom Used On NumberDescription Parameter 1 Effective CT-EDS Only 2000503-10 High puritytest Effective energy, 2 and Dual CT-EDS Only 2000503-30 object insertsin a material specific 3 Energy AT - L.S. Source, 2000503-50 variety ofsix (6) effective atomic Phantom Smiths or Rapiscan different materials.number (for dual energy platforms), 4 AT - R.S. Source, 2000503-70attenuation or CT Rapiscan Only number. 5 AT - Lower Source, 2000503-90Smiths Only 6 AT - L.S. Lower 2000503-110 Source, Rapiscan Only 7 AT -R.S. Lower 2000503-130 Source, Rapiscan Only 8 CT-EDS Only 2000504-10Cylinder for Mean CT number and measuring CT standard deviation fornumber consistency the cylinder and a rectangular reference object barfor measuring Resolution of the image SSP along the direction of beltmovement 9 Modulation AT - Center Lower 2000505-10 Metal test objectResolution of the Transfer Source, Smiths Only for measuring MTF imagewithin the X 10 Function AT - L.S. Lower 2000505-30 and Y planes. (MTF)Source, Rapiscan Phantom A Only 11 Phantom B AT - R.S. Lower 2000505-50Source, Rapiscan Only 12 Sheet on Belt CT-EDS Only 2000506-10 Plastictest object Evaluates the efficacy Phantom sheet of sheet detectionperformance 13 Wire CT-EDS Only 2000507-10 Different gauge Providesonscreen Resolution wire/metal step wire resolution (e.g., Phantomcylinder wire resolution; which is a CT adaptation of the ASTM792 UsefulPenetration Test) 14 Stability CT-EDS Only 2000508-10 explosivesimulants Provides assessment Phantom A 2000508-30 covering broad oftray impact on key 15 Stability density/Z effective CT metricsassociated Phantom B range. with detection within the explosive threatregion.

Table 1 describes fifteen (15) tray inserts which comprise the traycertification test set for both cabin baggage and hold baggage screeningapplications. Eight (8) tray inserts (Phantom No. 3-7, 9-11) arededicated to 2D X-ray systems, and seven (7) (Phantom No. 1-2, 8, 12-15)are for use on X-ray CT screening technologies. The mapping between thesystems and the tray insert (e.g., phantom) types are shown in thefollowing Table 2.

TABLE 2 System Phantom Type Measured Parameter CT/2D Effective and DualEffective energy, effective atomic number Energy (for dual-energy EDS),linear attenuation or CT number. CT Slice Sensitivity Measures contrastas a function of spatial Profile and resolution for both the X, Y, and Zplanes. CTN Consistency Measurement of the average CT number for areference object and variance of CT values within the reference object.2D Modulation Measures contrast as a function of spatial TransferFunction resolution for the X, Y, and Z planes. (MTF) A, B, and CPhantoms can support orthogonal and angled X-ray sources. CT Sheet onBelt Evaluates the efficacy of sheet detection performance CT WireResolution Provides onscreen wire resolution which is a CT adaptation ofthe European Standard Test Piece (STP) CT Stability A\B Uses 5 explosivesimulants for assessment of tray impact on key CT metrics associatedwith explosives detection.

The tray inserts listed in the above Table 1 and Table 2 will furtherdescribed below with reference to FIG. 2A-16B. The information relatedto each tray insert may be referred to corresponding items in the aboveTable 1 and Table 2.

FIGS. 2A and 2B illustrate a tray insert according to the secondembodiment of this application. FIG. 2A is a perspective and assembledview illustrating the tray insert 200 and FIG. 2B is an exploded viewillustrating the tray insert shown in the FIG. 2A. This tray insert 200is an Effective and Dual Energy tray insert corresponding to the phantomNo. 1 in Table 1. The tray insert 200 may comprise at least thefollowing components (from left-hand side to right hand side): asilicone part 212, an aluminum part 214, a graphite part 216 and twofoam plugs 218, 220. As shown in FIG. 2A, the silicone part 212, thealuminum part 214 and the graphite part 216 may be shown in grayscalecolors, while the foam plugs may be shown in white color separating theadjacent parts (212, 214 and 218). These components may be cylinders orhave an approximately cylinder-like appearance. Accordingly, in the body204 of the tray insert 200 there may be groove(s) 222 to accommodate theabove-discussed parts. These components may be used to evaluate themetrics discussed above. It should be noted that the sequence of thesilicone part 212, the aluminum part 214 and the graphite part 216 mayvary in different sequential orders and different variants of the trayinsert in this embodiment.

FIGS. 3A and 3B illustrate a tray insert 300 according to the thirdembodiment of this application. FIG. 3A is a perspective and assembledview illustrating the tray insert and FIG. 3B is an exploded viewillustrating the tray insert 300 shown in the FIG. 3A. This tray insert300 is an Effective and Dual Energy tray insert corresponding to thephantom No. 2 in Table 1. The tray insert 300 may include at least thefollowing components (from left-hand side to right hand side): a Teflonpart 312, a magnesium part 314, an acetal part 316 and two foam plugs318, 320. As shown in FIG. 3A, the Teflon part 312, the magnesium part314 and the acetal part 316 may be shown in grayscale colors, while thefoam plugs 318, 320 may be shown in white color. These components may becylinders or have an approximately cylinder-like appearance.Accordingly, there may be groove(s) 322 in the body of the tray insert300 to accommodate the above-discussed parts. These components may beused to evaluate the metrics discussed above. It should be noted thatthe sequence of the Teflon part 312, the magnesium part 314 and theacetal part 316 may vary in different sequential orders and in differentvariants of the tray insert in this embodiment.

FIGS. 4A and 4B illustrate a tray insert 400 according to the fourthembodiment of this application. FIG. 4A is a perspective and assembledview illustrating the tray insert and FIG. 4B is an exploded viewillustrating the tray insert 400 shown in the FIG. 4A. This tray insert400 is an Effective and Dual Energy tray insert corresponding to thephantom No. 3 in Table 1. The tray insert 400 may comprise at least thefollowing component: a module assembly 430 which is close to or attachedto the left-hand side wall 401A of the tray insert body 400. This moduleassembly may be a cube or have an approximately cube-like appearance.Accordingly, there may be groove(s) 422 in the body 402 of the trayinsert 400 to accommodate the module assembly. As shown in FIGS. 4A and4B, this module 430 assembly may include two parts 430A, 430B attachedto each other. In a variant of this embodiment, the module assembly 430may include more than two parts 430A, 430B which have been attached toeach other in sequence. The module assembly 430 may be used to evaluatethe metrics discussed above. The material of the module assembly 430 maybe made from at least one of the following material: silicone, aluminum,graphite, Teflon, magnesium, acetal, foam, etc. It should be noted thatthe sequence of the Teflon part, the magnesium part and the acetal partmay vary in different variants of the tray insert in this embodiment.

FIGS. 5A and 5B illustrate a tray insert 500 according to the fifthembodiment of this application. FIG. 5A is a perspective and assembledview illustrating the tray insert 500 and FIG. 5B is an exploded viewillustrating the tray insert 500 shown in the FIG. 5A. This tray insert500 is an Effective and Dual Energy tray insert corresponding to thephantom No. 4 in Table 1. A difference between this tray insert 500 andthat 400 shown in FIGS. 4A and 4B is that in this tray insert, themodule assembly 530 is closed to (proximal to) or attached to theright-hand side 501B wall of the tray insert body 502. Accordingly,there may be groove(s) 522 in the body 502 of the tray insert toaccommodate the module assembly 530. Also, as shown in FIGS. 5A and 5B,one part of the module assembly 530 comprises multiple holes 532 a-532 fshown in grayscale colors. Different materials may be filled into thoseholes 532 a-532 f respectively, and the materials may be selected fromthe following: silicone, aluminum, graphite, Teflon, magnesium, acetal,foam, etc. It should be noted that although there are six holes shown inthis embodiment, they are not intended to be exclusive or be limiting tothe tray insert. In a variant of this embodiment, there may more thansix holes 532 a-532 f or less than six holes in the module assembly 530.

FIGS. 6A and 6B illustrate a tray insert according to the sixthembodiment of this application. FIG. 6A is a perspective and assembledview illustrating the tray insert and FIG. 6B is an exploded viewillustrating the tray insert shown in the FIG. 6A. This tray insert 600is an Effective and Dual Energy tray insert corresponding to the phantomNo. 5 in Table 1. A difference between this tray insert 600 and thatshown 400 in FIGS. 4A and 4B is that in this tray insert 600, the moduleassembly 630 is close to (proximal to) or located at the bottom center640 of the tray insert body 602B. The module assembly 630 may alsocomprise multiple holes (not shown) as those shown in the fifthembodiment above. Materials in the holes may be referred to thosematerials disclosed in the fifth embodiment above.

FIGS. 7A and 7B illustrate a tray insert 700 according to the seventhembodiment of this application. FIG. 7A is a perspective and assembledview illustrating the tray insert 700 and FIG. 7B is an exploded viewillustrating the tray insert shown in the FIG. 7A. This tray insert 700is an Effective and Dual Energy tray insert corresponding to the phantomNo. 6 in Table 1. A difference between this tray insert 700 and 600 thatshown in FIGS. 6A and 6B is that in this tray insert 700, the moduleassembly 730 tilts at an angle to the left-hand side of the tray insert.The module assembly may also comprise two parts 730A, 730B as thoseshown in the fifth embodiment. The module assembly 730 may also comprisemultiple holes (not shown) as those shown in the fifth embodiment above.Materials in the holes may be referred to those materials disclosed inthe fifth embodiment above.

FIGS. 8A and 8B illustrate a tray insert 800 according to the eighthembodiment of this application. FIG. 8A is a perspective and assembledview illustrating the tray insert 800 and FIG. 8B is an exploded viewillustrating the tray insert shown in the FIG. 8A. This tray insert 800is an Effective and Dual Energy tray insert corresponding to the phantomNo. 7 in Table 1. A difference between this tray insert 800 and 700 thatshown in FIGS. 7A and 7B is that in this tray insert 800, the moduleassembly 830 tilts at an angle to the right-hand side of the trayinsert.

FIGS. 9A and 9B illustrate a tray insert according to the ninthembodiment of this application. FIG. 9A is a perspective and assembledview illustrating the tray insert and FIG. 9B is an exploded viewillustrating the tray insert shown in the FIG. 9A. This tray insert 900may be an Effective and Dual Energy tray insert corresponding to thephantom No. 8 in Table 1. The type of metrics extracted from the trayinsert 900 may also be Slice Sensitivity Profile and CTN Consistency.The tray insert 900 may include at least the following components: aslice sensitivity profile (SSP) bar 930 and a CT cylinder 932.Accordingly, there may be groove(s) 922 in the lower body 902B of thetray insert 900 to accommodate the bar 930 and the cylinder 932. Asshown in FIG. 9A, the SSP bar 930 and the CT cylinder 932 areperpendicular to each other. It should be noted that the positions ofthe SSP bar 930 and CT cylinder 932 shown in FIG. 9A are not intended tobe exclusive or be limiting to the tray insert 900, and their relativepositions may vary in different variants of this embodiment.

FIGS. 10A and 10B illustrate a tray insert 1000 according to the tenthembodiment of this application. FIG. 10A is a perspective and assembledview illustrating the tray insert 1000 and FIG. 10B is an exploded viewillustrating the tray insert 1000 shown in the FIG. 10A. This trayinsert may be a Modulation Transfer Function (MTF) Phantom Acorresponding to the phantom No. 9 in Table 1. The tray insert 1000 mayinclude at least the following components: a rod assembly 1030. The rodassembly 1030 may comprise multiple rods 1030A to 1030C attached to eachother. As shown in FIG. 10A, the rod assembly 1030 has a cross-likeappearance or shape. It should be not noted that the appearance of therod assembly 1030 may other appearances as long as the rod assembly 1030may realize its function discussed in this application. The rod assembly1030 may be allocated in the bottom 1002B of the tray insert 1000.Accordingly, there is a cross-like groove 1022 in the bottom 1002B ofthe tray insert to accommodate the rod assembly 1030.

FIGS. 11A and 11B illustrate a tray insert 1100 according to theeleventh embodiment of this application. FIG. 11A is a perspective andassembled view illustrating the tray insert 1100 and FIG. 11B is anexploded view illustrating the tray insert 1100 shown in the FIG. 11A.This tray insert 1100 may be a Modulation Transfer Function (MTF)Phantom B corresponding to the phantom No. 10 in Table 1. A differencebetween this tray insert 1100 and the one 1000 in the tenth embodimentis the rod assembly 1130 tilts to the left-hand side 1103A of the trayinsert 1100. Accordingly, the groove 1122 in the bottom 1102B of thetray insert may vary to accommodate the rod assembly 1130. That is, asshown in FIG. 11B, the groove 1122 is not an exact shape of a cross.

FIGS. 12A and 12B illustrate a tray insert 1200 according to the twelfthembodiment of this application. FIG. 12A is a perspective and assembledview illustrating the tray insert 1200 and FIG. 12B is an exploded viewillustrating the tray insert shown in the FIG. 12A. This tray insert1200 may be a Modulation Transfer Function (MTF) Phantom B correspondingto the phantom No. 11 in Table 1. A difference between this tray insert1200 and the one 1100 in the eleventh embodiment is the rod assembly1230 tilts to the right-hand side of the tray insert 1200.

FIGS. 13A and 13B illustrate a tray insert 1300 according to thethirteenth embodiment of this application. FIG. 13A is a perspective andassembled view illustrating the tray insert and FIG. 13B is an explodedview illustrating the tray insert shown in the FIG. 13A. This trayinsert 1300 is a Sheet on Belt tray insert corresponding to the phantomNo. 12 in Table 1. The tray insert 1300 may include at least thefollowing components (from top to bottom): a covering 1302, a plate 1303and a sheet 1304. Preferably, the plate 1303 is 10 mm Thick. Preferably,the plate 1303 is made from Acetron®. The plate's 1303 thickness mayvary in different variants of this embodiment. Also, the material(s) ofthe plate 1303 may also vary in different variants of this embodiment,which is sandwiched between the covering 1302 and the sheet 1304.

FIGS. 14A and 14B illustrate a tray insert 1400 according to the fourthembodiment of this application. FIG. 14A is a perspective and assembledview illustrating the tray insert 1400 and FIG. 14B is an exploded viewillustrating the tray insert 1300 shown in the FIG. 13A. This trayinsert 1400 is a Wire Resolution tray insert corresponding to thephantom No. 13 in Table 1. The tray insert 1400 may include at least thefollowing components: a CT wire resolution probe assembly 1440.Preferably, the assembly 1440 is installed at the right end 1408 of thetray insert 1400.

FIGS. 15A and 15B illustrate a tray insert 1500 according to thefifteenth embodiment of this application. FIG. 15A is a perspective andassembled view illustrating the tray insert 1500 and FIG. 15B is anexploded view illustrating the tray insert 1500 shown in the FIG. 15A.This tray insert 1500 is a Stability Phantom A corresponding to thephantom No. 14 in Table 1. The tray insert 1500 may include at least thefollowing components: two simulants 1550A, 1550B (e.g., Tango Whiskey &Tango Hotel shown in FIGS. 15A and 15B) positioned in grooves 1522A to1522C, respectively. As shown in FIGS. 15A and 15B, there is a distanced between the simulants 1550A, 1550B.

FIGS. 16A and 16B illustrate a tray insert 1600 according to thesixteenth embodiment of this application. FIG. 16A is a perspective andassembled view illustrating the tray insert 1600 and FIG. 16B is anexploded view illustrating the tray insert shown in the FIG. 16A. Thistray insert 1600 is a Stability Phantom B corresponding to the phantomNo. 15 in Table 1. The tray insert 1600 may include at least thefollowing components: three simulants 1650A, 1650B, 1650C (e.g., TangoEcho, Sierra India & Sierra Echo shown in FIGS. 16A and 16B) positionedin grooves 1622A to 1622C, respectively. As shown in FIGS. 16A and 16B,there is a distance d between the simulants 1650A, 1650B and 1650C.

Data collected through the tray inserts 1600 may support baseline dataset development and statistical test methodology design. Once processed,data will be analyzed for inconsistencies in the establishment of abaseline data set to support test and evaluation of new X-raysystem/tray configurations. Based on data distributions, appropriatestatistical tests for each of the image metric parameters may bedesigned.

In embodiments, a blind test on a new X-ray system/tray variantconfiguration using the test phantoms and the established testmethodology and baseline dataset may be performed. A tray insertdesigner or manufacturer may coordinate the collection of additionaldata on an X-ray system and tray variant (could be certified oruncertified, or both) and conduct a test on the collected data tovalidate the test approach. X-ray system/tray configurations will bepre-evaluated by the ECAC certifying body and assigned a certified oruncertified status, but this information will not be shared until afterthe contractor test/analysis is complete. Once the analysis/test iscomplete, results shall be compared with the ECAC results andrefinements to the statistical test methodology shall be performed bythe designer/manufacturer to optimize established acceptance thresholdsdiscussed above.

FIGS. 17A and 17B illustrate a tray insert 1700 used with a tray 1750for tray certification. For example, a tray inert 1700 may be orientedaccording to the direction of the movement of the conveyor belt, whichthe tray insert 1700 may be centrally placed in tray 1750. The loadedtray 1750 (including the tray insert 1700) may be centrally placed onthe conveyor belt of the X-ray system for image quality data and metricsextraction, to evaluate if the image quality of the tray insert 1700 isnegatively impacted by the tray 1750 used in the evaluation, bycomparing the extracted metrics with the baseline metrics of theapproved trays which have been certified by the ECAC.

The technical effect which may be obtained by using the tray inserts toverify trays is briefly described as follows. The old certificationmethods already known on the market essentially run an experiment withexplosives or explosive simulants to determine if the presence of a traywill have an effect on the X-ray system's explosive detection ability.This requires considerable amounts of time and is resulting in a backlogof systems waiting to be deployed making it difficult for many airportsto meet corresponding mandates for migrating to newer X-ray technology.By contrast, the solution using the tray inserts may perform acertification test within minutes, and may be conducted at amanufacture's location, airports or any location where the X-ray systemis installed. Typically, in such a solution, only approximately 10minutes may be needed to collect images, run the image quality andstatistical analysis and generate a report. FIGS. 18-20B may illustratethe tray certification method and algorithms as follows.

FIG. 18 is an overall flowchart depicting a method and an algorithm forcertifying a candidate tray using image quality (IQ) data of alreadycertified tray by an X-ray system.

In step 1802, prior to data collection, the X-ray system may be properlyprepared to comply with data collection accuracy. For example, the trayunder test (TUT) (i.e., certified tray or candidate tray) may bemeasured to confirm meeting the tray size requirements listed in anapplicable user manual for one or more X-ray systems. Any oversized orundersized dimensions may impact placement of a tray insert or mayobstruct the imaging or feeding movement of the tray in the X-raysystem. In an example, the TUT should have a minimum tray size measuredas 17 inches×8.5 inches. For Advanced Technology (AT) two-dimensional(2D) X-ray, the tray height (i.e., certified tray or candidate tray)should be at least 4 inches tall.

The preparation step 1802 may further include a selection of a correcttray insert (phantoms) to be used for the tray certification:

a. Select correct phantoms (i.e., tray insert)

-   -   i. Identify the test phantoms required for data collection for        the scanner system being evaluated.    -   ii. Phantoms specifically for Advanced Technology (AT) X-ray and        Computer Tomography (CT) X-ray. The labels on the phantoms may        indicate whether they are to be used with AT or CT X-ray.    -   iii. For AT X-ray need to select phantoms based on X-ray source        location

TABLE 3 Dual Energy MTF Source Location Phantom Phantom Left #3 n/aRight #4 n/a Bottom #5  #9 Lower Left #6 #10 Lower Right #7 #11

2D Phantom Selection (System Viewed Along Direction of Belt Travel):

b. Ensure X-ray system is running correctly

-   -   i. Startup scanner system according to manufacturer's        instructions. Process the Operational Test Kit (OTK) according        to the manufacturer's instructions. System may be required to be        switched to OTK mode.    -   ii. For additional assurance, it is suggested that the machine        pass a Tray User's Manual system.

In step 1804, baseline image data may be collected:

a. Capture metadata

-   -   i. Use the DataCollectionPlanTemplate file and fill out the        relevant metadata for the system. Refer to Tray User Manual for        a list of the required metadata (X-ray system serial number,        ambient temperature and humidity).

b. Find and mark the centerline of the X-ray system.

-   -   i. Mark the centerline of the scanner system belt for proper        positioning of the certified tray on the belt. Refer to User        Manual for measurement guidance.

c. Position phantom on X-ray system belt.

-   -   i. Do not use the tray at this point.    -   ii. Orient and place the phantom on the scanner system's belt        according to the centerline. Refer to Tray User Manual for        additional detail.    -   iii. Phantom must be centered within +/−2 cm.    -   iv. Phantom must be aligned straight with the direction of the        belt +/−2 degrees.

d. Run each phantom through the system 100 times

-   -   i. Process the first phantom through the scanner system 100        times. Tally the number of scans. Record the date and time for        each image collected.    -   ii. Repeat with each phantom.

e. Copy image files and record remaining data to be established asbaseline data.

-   -   i. Download the image files from the scanner system according to        the manufacturer's instructions. Verify there are 100 images        from each of the phantoms.    -   ii. Record the remaining metadata in the        DataCollectionPlanTemplate file. Refer to User Manual for a list        of the required metadata.

In step 1806, candidate tray (Tray Under Test or TUT) image data may becollected:

a. Capture metadata

-   -   i. Use the DataCollectionPlanTemplate file and fill out the        relevant metadata for the system. Refer to Tray User Manual for        a list of the required metadata (X-ray system serial number,        ambient temperature and humidity).

b. Find and mark the centerline of the X-ray system

-   -   i. Mark the centerline of the scanner system belt for proper        positioning of the candidate tray on the belt. Refer to User        Manual for measurement guidance.

c. Prepare tray

-   -   i. Prepare to position the phantom inside the tray by measuring        out the center of the tray. Refer to Tray User Manual for        measurement guidance.    -   ii. Mark the tray based off the measurements to identify the        placement of the phantom. Affix the Velcro fasteners to the        inside of the tray. Refer to User Manual.

d. Position phantom inside the tray.

-   -   i. Orient, place, and affix the phantom inside the tray. Refer        to Tray User Manual for additional detail.

e. Position tray on X-ray system belt.

-   -   i. Orient and place the tray containing the phantom on the        scanner system's belt according to the centerline. Refer to Tray        User Manual for additional detail.    -   ii. Tray must be centered within +/−2 cm    -   iii. Tray must be aligned straight with the direction of the        belt +/−2 degrees.

f. Run each phantom through the system 100 times

-   -   i. Process the first phantom through the scanner system 100        times. Tally the number of scans. Record the date and time for        each image collected.    -   ii. Repeat with each phantom

g. Copy image files and record remaining data.

-   -   i. Download the image files from the scanner system according to        the manufacturer's instructions. Verify there are 100 images        from each of the phantoms.    -   ii. Record the remaining metadata in the        DataCollectionPlanTemplate file. Refer to Step 9 in the Section        5.4 of the VIQ User Manual for a list of the required metadata.

In step 1808, image quality metrics of the TUT may be calculated. Morespecifically, a “z-slice” or a cross-section image of an object (i.e.,the TUT) in the xy-plane at a specific z-location may be provideddirectly from the Explosive Detection Systems (EDS) to form volumetricdata. A two-dimensional or one-dimensional projection may be calculatedfrom three-dimensional data as follows:

Constructing Projections:

-   -   a. Assume that the volumetric image data are represented by a        three-dimensional matrix I(x,y,z)    -   b. Two-dimensional projections, I_(xy), I_(xz), I_(yz)        -   i. These are the projections of the three-dimensional            volumetric image data onto a single plane.        -   ii. I_(xy)(x,y)=Σ_(z)I(x,y,z)            -   1. I_(xy) is the projection onto the xy-plane. This is                akin to looking through the object from front to back.        -   iii. I_(xz)(x,z)=Σ_(y)I(x,y,z)            -   1. I_(xz) is the projection onto the xz-plane. This is                akin to looking through the object from top to bottom.        -   iv. I_(yz)(y,z)=Σ_(x)I(x,y,z)            -   1. I_(yz) is the projection onto the yz-plane. This is                akin to looking through the object from side to side.    -   c. One-dimensional projections, I_(x), I_(y), I_(z)        -   i. These are the projections of the three-dimensional            volumetric image data onto a single plane        -   ii. I_(x)(x)=Σ_(y)Σ_(z)I(x, y, z)            -   1. I_(x) is the projection onto the x-axis        -   iii. I_(y)(y)=Σ_(z)Σ_(x)I(x,y,z)            -   1. I_(y) is the projection onto the y-axis        -   iv. I_(z)(z)=Σ_(x)Σ_(y)I(x, y, z)            -   1. I_(z) is the projection onto the z-axis

A rolling average (also moving average, running average) is acalculation to analyze data points by creating a series of averages ofdifferent subsets of the full data set. Given a series of data pointsand a fixed window size (subset size), the first element of the rollingaverage is obtained by taking the average of the initial fixed windowsize of the series. The subset is the modified by shifting forward,i.e., excluding the first data point in the subset, and including thenext value outside of the subset.

The greatest rolling average routine finds the subset of data with thegreatest average. This type of calculation is typically achieved with alinear convolution of two one-dimensional sequences (e.g. cony in MATLABor numpy.convolve in Python).

The above paragraphs (creating image projections, calculating greatestrolling average) describe common methods used by multiple of the metricextraction routines. Another common method, calculating a mean and astandard deviation of CTN values within a volume of interest, may beperformed as follows:

-   -   a. Assume a collection of z-slices in which regions of interest        have been identified and masking has already been applied to        zero out any pixels outside of the regions of interest.    -   b. Initialize the following running totals variables to zero:        CTN_(total), CTN² _(total), and number of pixels        (N_(counts total)).    -   c. For each slice, z, within the volume of interest:        -   i. Use the identified region of interest for the slice to            create a mask, containing 1's for all pixels within the            region of interest and 0's for all pixels outside.        -   ii. Apply the mask to the slice image, leaving all pixels            within the region of interest intact and setting all pixels            outside to zero.        -   iii. Count the number of non-zero pixels remaining in slice            z (N_(non-zero, z)), sum the CTN and CTN² values for these            pixels, and add these to the running totals.

$\begin{matrix}{{{CT}N_{total}} = {{CTN}_{total} + {\underset{i = 1}{\sum\limits^{N_{{{non} - {zero}},z}}}{CTN_{i}}}}} \\{{{CT}N_{total}^{2}} = {{{CT}N_{total}^{2}} + {\underset{i = 1}{\sum\limits^{N_{{{non} - {zero}},z}}}{CTN_{i}^{2}}}}} \\{N_{{counts}{total}} = {N_{{counts}{total}} + N_{{non} - {zero}z}}}\end{matrix}$

-   -   d. Once all slices within the volume of interest have been        processed, calculate the final mean and standard deviation        metrics:

$\overset{\_}{CTN} = {{CTN}_{total}/N_{{counts}{total}}}$$\sigma_{CTN} = \sqrt{{{CTN}_{total}^{2}/N_{{counts}{total}}} - {\overset{\_}{CTN}}^{2}}$

In step 1810, a report of the image quality of the TUT may be generatedto indicate whether the TUT may be certified for use in the X-raysystem. If not, what design features may be recommended for adjustments.

FIG. 19A-19F illustrate examples of X-ray artifacts impacted by traydesign. In an example, each tray insert 1900 may be positionedsubstantially in center of a tray under test (TUT) 1950, and the TUT1950 may be positioned in middle of scanning path of a system belt whenperforming the scanning in generating the image data.

More specifically, FIGS. 19A-19C illustrate an X-ray scan of a trayinsert 1900 (i.e., phantom #12) positioned in the center of the TUT 1950in different projected planes (xy plane, xz plane and yz plane), whereintwo-dimensional (2-D) projection images of the TUT 1950 with the trayinsert 1900 may be reconstructed from the volumetric image data scannedin each of the different projected planes.

FIGS. 19D-19F illustrate examples of X-ray artifacts impacted by thetray design after image data reconstruction. FIG. 19D shows a slicedimage profile (in yz plane) of the tray under test (TUT) 1950 having atray insert 1900 (i.e., Phantom #1) centrally laid in the TUT. FIG. 19Eshows that an X-ray artifact 1960, which may be shown as a streakfeature through the tray insert 1900, which may be caused by one or morestructural support 1970 beneath the TUT 1950. FIG. 19F is a close-upimage showing the X-ray artifact 1960 caused by the one or morestructural support 1970. The X-ray artifact 1960 may be reported asfeedback to the tray manufacturer for redesign or modification until thetray passes certification.

FIGS. 20A-20B illustrate an example of metrics extraction process toinvestigate certain tray design imp acts. As pointed out in thedescription of FIG. 19D to 19F, some support structures 2070 (e.g.,legs, stands) formed beneath the TUT 2050 may come in different shapes,such as conical shape or rectangular shape may overlap with a selectedtray insert 2000 to generate X-ray artifacts. The extracted metrics fromthe image of the multiple parts (such as the Silicone part, Aluminumpart, Graphite part, or the Teflon part, Magnesium part and Acetal part)within the selected test insert 2000 may have varying degrees of impactswhich may still be within an acceptable threshold limit, and thus may beignored in the calculations.

Image quality metrics may be extracted from a particular tray insert,using a tray certification algorithm. In an example, the traycertification Image Quality (IQ) algorithm may extract metrics fromscanned images of the tray under test (TUT) including the tray insertwhich are generated from the Advanced Technology (AT) two-dimensional(2D) X-ray. Some examples of the metrics or parameters which areextracted from the AT machine may include:

Material Linear Attenuation: The linear attenuation coefficient ismeasured for the multiple parts inside the tray insert, such as 6cylinders of high purity (Copper, Aluminum, Graphite, Teflon, Magnesium,Acetal). This attenuation coefficient may be measured for both the highand low energy x-ray beams.

Z-Effective linearity: From the previously measured linear attenuationcoefficients a linear fit may be performed of the high-to-low ratiosversus the materials effective atomic number.

Effective Energy: The effective beam energy for the high and low energysources may be calculated by looking at the linear attenuationcoefficients across the set of high purity material cylinders.

MTF: The Modulation Transfer Function may be measured across a spectrumof frequencies (spatial resolutions).

Simulant Linear Attenuation: The liner attenuation coefficient ismeasured for 6 different types of explosive simulants.

In another example, the tray certification Image Quality (IQ) algorithmmay extract metrics from scanned images of the tray under test (TUT)including the tray insert which are generated from the computedtomography (CT) three-dimensional (3D) X-ray. Some of the metrics orparameters which are extracted from the CT EDS, in addition to thosedescribed above for the AT machine, may include:

Material CT Number: The CT number may be measured for the multiple partsof the 6 cylinders of high purity (Silicone, Aluminum, Graphite, Teflon,Magnesium, Acetal) inside the selected tray insert (e.g., using Phantom#1 and 2).

Slice Sensitivity Profile: The spatial resolution along the direction ofbelt travel may be measured by imaging a slanted acetal bar.

CT Consistency: The consistency of the measured CT number may bedetermined by measuring its values across a large cylinder of uniformAcetal.

Simulant CT Number: The CT number may be measured (using Phantom #12-15)for 6 different types of explosive simulants (TNT, Ammonium Nitrate, andNitroglycerine, to name a few).

The below information describes the process of image quality metriccalculation for the TUT using certain selected phantom as tray insertsfrom Table 1:

Tray Phantoms 1 and 2

-   -   a. Estimate roughly which slices contain the phantom.        -   i. Project the volumetric image data onto the z-axis to            obtain the one-dimensional z-projection, I_(z).        -   ii. Estimate the slices containing the phantom.            -   1. Use a window size equal to the length of the phantom                (17 in=431.8 mm)            -   2. Define the slices containing the phantom as the                subset (with size equal to the window size defined                above) of I_(z) with the greatest rolling average.        -   iii. Exclude slices from the first and last 1.5 in (38.1 mm)            to exclude the front and back caps of the phantom.    -   b. Determine the centerline of the phantom, essentially giving        the orientation of the phantom in the xz- and yz-planes. This        step will also identify circular regions of interest (ROI)        within slices.        -   i. For each z-slice, attempt to identify a circle with            radius approximately equal to that of one of the test            objects (i.e. the cylinders, whose radius ˜15.875 mm). Only            circles with the appropriate radii will be included.            -   1. Note, a circle will not be found in every slice, as                the test objects will not be found in every slice.            -   2. Appropriate thresholds should be utilized in                combination with methods to eliminate small connect                objects and holes to rid the image of as much noise as                possible.        -   ii. Using the collection of found circles, fit a straight            line through their centers. This defines the centerline of            the phantom.            -   1. Fit separately the collections of (x_(circle),                z_(slice)) and (y_(circle), z_(slice)). The result will                be a line representing the orientation of the phantom in                the xz-plane and a line representing the orientation of                the phantom in the yz-plane.        -   iii. The identified circular regions of interest should be            store din such a manner as they can be easily retrieved for            a given slice. These will be utilized later when performing            the actual metric extraction calculation.        -   iv. The centerline determined here can be utilized to ensure            that the object is presented at an acceptable orientation. A            flag can be input to fail the analysis if the presentation            is not within thresholds.    -   c. Identify the z-slices containing the three test objects        (z-ROIs).        -   i. Create a two-dimensional projection of the volumetric            image data onto the xz-plane, I_(xz).        -   ii. Take the CTN threshold to be the sum of the CTN of            air/foam plus the average CTN of I_(xz).            -   1. Approximate the CTN of air as the most frequent CTN                value in the I_(xz).                -   a. The phantom is mostly empty, so the most frequent                    value encountered should be that of air/foam.        -   iii. Using the threshold CTN, create a mask which will            select only pixels whose CTN value is greater than that of            the threshold.        -   iv. Apply this mask to I_(xz), leaving approximately only            pixels containing the test articles, with all other pixels            empty.        -   v. Use an acceptable image processing algorithm to locate            the three test objects, which will appear as rectangles in            the xz-projection.    -   d. Knowing the length of each test object cylinder (˜76.2 mm)        and the spacing between the cylinders (˜37.846 mm), ensure that        the located regions of interest roughly equal what would be        expected.    -   e. Using the z-slices containing the objects (z-ROIs) together        with the previously found circular regions of interest within        each slice (which together define the three volumes of        interest), extract the metrics from each test object.    -   i. This amounts to calculating the average and standard        deviation of the CTN values within the volume of interest.

Tray Phantom 8:

-   -   f. CT Value Consistency        -   i. This set of metrics is extracted from the cylindrical            test object within the phantom.        -   ii. Obtain 64 images of the cylindrical test object. If the            system cannot create 64 images (due to slice size, etc.),            take the maximum number possible while avoiding the leading            and trailing edges of the object.        -   iii. Define a circle of interest in each image that has a            radius 10 mm less than the radius of the test object,            centered on the test object within the image. Define the            group of voxels that are completely enclosed within the            circle of interest for each image.        -   iv. Calculate the mean and standard deviation of the CT            value for each voxel group. Calculate the median and the            standard deviations of the set of means and the set of            standard deviations.        -   g. Slice Sensitivity Profile (SSP)            -   i. Locate the test object and designate a                right-rectangular volume ROI, I(x,y,z), containing the                leading, trailing, top and bottom faces of the test                object but not the side faces.            -   ii. Generate a coronal image, I_(c)(x,z) of the test                object by summing all CT values within the ROI along the                y-axis. The coronal image is oriented so that each                horizontal row is specified by a different x value.            -   iii. Computer the centerline of the test object:                -   1. Calculate a center of mass, com_(x), for each row                    (in z) in the ROI as shown in the following                    equation:

${a.{com}_{x}} = \frac{{\sum}_{z = 0}^{z_{\max}}{z \cdot {I_{C}\left( {x,z} \right)}}}{{\sum}_{z = 0}^{z_{\max}}{I_{c}\left( {x,z} \right)}}$

-   -   -   -   -    b. where x is the x coordinate of the row                -    i. z_(max) is the maximum z value in the ROI                -   2. Fit a line in the xz-plane to the set of all                    com_(x).

            -   iv. For each row in the ROI, compute the edge spread                function as follows:                -   1. Compute the z distance of each pixel in the ROI                    from the centerline.                -   2. For each row, scale the pixel values by the                    maximum CT value measured within the ROI to correct                    for beam hardening and scatter effects.                -   3. Generate a table of all pixel values within the                    ROI in the order of their distance from the                    centerline.                -   4. Using the methods specified in ASTM                    E1695-95 (2013) starting at 7.1.1.5 and continuing                    through 7.1.3.3, generate the edge response                    function, point spread function, and the modulation                    transfer function using grayscale images.

Tray Phantom 12:

-   -   A. Estimate roughly which slices contain the phantom.        -   a. Project the volumetric image data onto the z-axis to            obtain the one-dimensional z-projection, T.            -   i. See “Constructing projections” in                GeneralTechniquesAndDefinitions.docx for more                information.        -   b. Estimate the slices containing the phantom.            -   i. Use a window size equal to the length of the phantom                (17 in=431.8 mm)        -   ii. Define the slices containing the phantom as the subset            (with size equal to the window size defined above) of I_(z)            with the greatest rolling average.        -   iii. See “Greatest rolling average” in            GeneralTechniquesAndDefinitions.docx for more information.    -   B. Identify the z-slices containing the test object (z-ROI)        -   a. Approximate the center of the phantom, which is also the            approximate center of the test object, as the center of the            slices found above.        -   b. The length of the test object acetal sheet, not including            the notched ends, is 14.94 in (379.476 mm). To ensure the            region of interest is sufficiently far from the ends of the            test object, define the limits of the z-ROI to be within 6            in (152.4 mm) of the center slice (such that the entire            z-ROI has a length of 12 in).    -   C. Define a rough region of interest in the x-direction (x-ROI).        -   a. The x-ROI is only used to build clean projections for            other portions of the code.        -   b. Project the volumetric image data onto the x-axis to            obtain the one-dimensional x-projection, I_(x).        -   c. Estimate the x-pixels containing the phantom.            -   i. Use a window size equal to the width of the phantom                (5 in=127 mm)            -   ii. Define the pixels containing the phantom as the                subset (with size equal to the window size defined                above) of I_(x) with the greatest rolling average.        -   d. To ensure the x region of interest is away from the sides            of the test object, define the rough x-ROI as within 2 in            (50.8 mm) of the center (i.e. the width of the x-ROI is 4            in=101.6 mm).    -   D. Note: Treatment of the y-direction is different from the x-        and z-directions, as it was discovered that the y-value of the        center of the test object can vary with z.        -   a. i.e., when looking at a yz-projection image, the test            object is not always completely horizontal.    -   E. Determine the centerline of the phantom, essentially giving        the orientation of the phantom in the xz- and yz-planes. This        step will also identify rectangular regions of interest (ROI)        within slices.        -   a. Calculate the CTN threshold to be used in the masking.            -   i. Use an xy-projection which includes only slices from                the z-ROI, I_(xy clean). This will help to smooth out                any bright spots which may appear in single slices.                -   1. Assuming the first z-slice of z-ROI is z₁ and the                    last is z₂:                -   2. I_(xy clean)(x,y)=Σ_(z=z) ₁ ^(z=z) ² I(x,y,z)            -   ii. Determine the least bright pixel in the row                containing the center of the test object,                CTN_(min of max row).                -   1. Construct a one-dimension projection onto the                    y-axis, I_(y) clean, using only slices within z-ROI                    and only pixels within the rough x-ROI. Assuming the                    first pixel of x-ROI is x₁ and the last is x₂:                -   2. I_(y clean)(y)=Σ_(x=x) ₁ ^(x=x) ²                    I_(xy clean)(x,y)                -   3. Using the typical rolling average technique with                    I_(y) clean, find the approximate center of the test                    object sheet in the y-direction, y_(center, approx).                -    a. Note, some care must be taken, as for baseline                    images the sheet is essentially directly on the                    belt, not above the belt at a distance equal to the                    height of the tray.                -    b. See “Greatest rolling average” in                    GeneralTechniquesAndDefinitions.docx for more                    information.                -   4. In a window surrounding the center                    y_(center, approx), determine the brightest overall                    row in y, and set that as the center of the test                    object in the y-direction, y_(center).                -   5. Find the minimum CTN value within the rough x-ROI                    for this brightest row containing y_(center).                -    a. Define as CTN_(min of max row)            -   iii. Determine the background CTN, CTN_(background)                -   1. Look away from the test article in I_(xy clean).                    The width of the test object is 5.0 in (127 mm). To                    ensure the background windows are outside of the                    test object, define the background regions of                    interest to not be within 3 in of the center of the                    test object in the x-direction.                -   2. Find the maximum CTN values of the background in                    the region to the left and that to the right of the                    test object.                -   3. Define the background CTN, CTN_(background),                    value as the maximum of these two.            -   iv. Ensure that the CTN_(background) is less than                CTN_(min of max row).                -   1. If this is not true, then applying the threshold                    will make the test object disappear.            -   v. Define the CTN threshold, CTN_(threshold) to be the                background CTN found above divided by the number of                slices included in the projection image (i.e. divided by                z₂−z₁+1).                -   1. CTN_(threshold)=CTN_(background)/(z₂−z₁+1)                -   2. The division makes it such that the threshold is                    appropriate to apply to individual voxels, not                    voxels which have been projected down into pixels.            -   vi. Define the standard CTN threshold, CTN_(std), as                0.5(CTN_(water)+CTN_(air)).            -   vii. If the CTN_(threshold) found is less than                CTN_(std), then use CTN_(std) instead.        -   b. For each slice in the z-ROI, find the rectangle within            the image with dimensions closely matching those of the test            object.            -   i. Apply CTN_(threshold) to create a black and white                image.            -   ii. Using appropriate image processing algorithm (e.g.                bwareaopen in MATLAB), remove any connected areas less                than the area threshold, which is currently set at 500                mm²            -   iii. In a similar fashion, remove any connected holes.                -   1. This can be achieved by inverting the image                    (imcomplement in MATLAB) and utilizing the same                    technique as above for removing connected areas.                    Invert the image once again to recover the original                    BW image without any connected holes or areas.            -   iv. Use appropriate image processing algorithms to                identify rectangular objects within the remaining BW                image.            -   v. Enforce some quality metric to ensure that the shape                recovered by the algorithm looks as expected. If                multiple exist which pass the quality standard, select                the best (although this should not happen). We implement                the following procedure:                -   1. Use regionprops to extract properties of                    identified regions within the image. At a minimum,                    ‘Perimeter’ and ‘Area’ must be collected.                -   2. For a rectangle with height a and width b                -    a. Area: A=ab                -    b. Perimeter: P=2(a+b)                -   3. Using the above equations for area and perimeter,                    one can solve for either the height or width, and                    find:

${a.0} = {a^{2} - {\frac{P}{2}a} + A}$

-   -   -   -   -   4. Solving the above quadratic equation yields two                    roots, which represent the height a and width b of                    the region (assuming the region to be rectangular).                -   5. Take the actual height and width of the test                    object to be a_(actual) and b_(actual), and the                    height of width of the identified region within the                    image to be a_(region) and b_(region). The quality                    metric determining how closely the found region                    matches the shape of the test object is:

${a.{quality}} = \sqrt{\left( \frac{a_{actual} - a_{region}}{a_{actual}} \right)^{2} + \left( \frac{b_{actual} - b_{region}}{b_{actual}} \right)^{2}}$

-   -   -   -   -   6. For now, the maximum acceptable quality is set to                    1.0. This is still a loose restriction which could                    be tightened, but it seems to be sufficient.

    -   F. Using the z-slices containing the test object (z-ROI)        together with the previously found rectangular regions of        interest within each slice (which together define the volume of        interest), extract the metrics from the test object.        -   a. This amounts to calculating the average and standard            deviation of the CTN values within the region of interest.

Tray Phantom 14 and 15:

Note: This is very similar to the methods used for Tray Phantoms 1 & 2

-   -   A. Estimate roughly which slices contain the phantom.        -   a. Project the volumetric image data onto the z-axis to            obtain the one-dimensional z-projection, I_(z).        -   b. Estimate the slices containing the phantom.            -   i. Use a window size equal to the length of the phantom                (17 in=431.8 mm)            -   ii. Define the slices containing the phantom as the                subset (with size equal to the window size defined                above) of I_(z) with the greatest rolling average.        -   c. Exclude slices from the first and last 1.25 in (31.75 mm)            to exclude the front and back caps of the phantom.    -   B. Determine the centerline of the phantom, essentially giving        the orientation of the phantom in the xz- and yz-planes. This        step will also identify circular regions of interest (ROI)        within slices.        -   a. For each z-slice, attempt to identify a circle with            radius approximately equal to that of one of the test            objects (i.e. the containers, whose body radius ˜42.418 mm            and cap radius is 46.863 mm). Only circles with the            appropriate radii will be included.            -   i. Note, a circle will not be found in every slice, as                the test objects will not be found in every slice.            -   ii. Appropriate thresholds should be utilized in                combination with methods to eliminate small connect                objects and holes to rid the image of as much noise as                possible.        -   b. Using the collection of found circles, fit a straight            line through their centers. This defines the centerline of            the phantom.            -   i. Fit separately the collections of (x_(circle),                z_(slice)) and (y_(cirele), z_(slice)). The result will                be a line representing the orientation of the phantom in                the xz-plane and a line representing the orientation of                the phantom in the yz-plane.        -   c. The identified circular regions of interest should be            stored in such a manner as they can be easily retrieved for            a given slice. These will be utilized later when performing            the actual metric extraction calculation.        -   d. The centerline determined here can be utilized to ensure            that the object is presented at an acceptable orientation. A            flag can be input to fail the analysis if the presentation            is not within thresholds.    -   C. Identify the z-slices containing the three test objects        (z-ROIs).        -   a. Create a two-dimensional projection of the volumetric            image data onto the xz-plane, I_(xz).        -   b. the average CTN of I_(xz).            -   i. Approximate the CTN of air as the most frequent CTN                value in the I_(xz).                -   1. The phantom is mostly empty, so the most frequent                    value encountered should be that of air/foam.        -   c. Using the threshold CTN, create a mask which will select            only pixels whose CTN value is greater than that of the            threshold.        -   d. Apply this mask to I_(xz), leaving approximately only            pixels containing the test articles, with all other pixels            empty.        -   e. Use an acceptable image processing algorithm to locate            the three test objects, which will appear as rectangles in            the xz-projection.            -   i. For Tray Phantom 14, assert two test objects found.                For Tray Phantom 15, assert three test objects found.    -   D. Knowing the length of each test object cylinder (˜83.312 mm)        and the spacing between the cylinders (˜70.866 mm), ensure that        the located regions of interest roughly equal what would be        expected.        -   a. Shift the minimum (front-end) of each z-ROI by the length            of the cap (˜17.526 mm) so as to include only the contents            of the test object in the measurement and not the cap.    -   E. Using the z-slices containing the objects (z-ROIs) together        with the previously found circular regions of interest within        each slice (which together define the three volumes of        interest), extract the metrics from each test object.        -   a. This amounts to calculating the average and standard            deviation of the CTN values within the volume of interest.

The above description are exemplary embodiments, which are not to beconstrued as limiting to only CT metrics, other embodiments orvariations for the AT may similarly be derived under the interpretationof the ordinary skilled in the art.

It will be appreciated that the terminology used in the presentapplication is for the purpose of describing particular embodiments andis not intended to limit the application. The singular forms “a”, “the”,and “the” may be intended to comprise a plurality of elements. The terms“including” and “comprising” are intended to include a non-exclusiveinclusion. Although the present application is described in detail withreference to the foregoing embodiments, it will be appreciated thatthose foregoing embodiments may be modified, and such modifications donot deviate from the scope of the present application.

What is claimed is:
 1. A computer implemented method to quantify X-rayscanner system's image quality impact by a candidate tray, the methodcomprising: executing by at least a processor in a computer, at leastone code stored in a non-transitory computer-readable medium whichcauses the computer to control an X-ray scanner system to quantifycandidate tray impact, comprising: collecting baseline image data forthe X-ray scanner system that comprises characteristic image qualitydata collected from a plurality of certified trays that have beenscanned with a plurality of selected tray inserts; collecting one ormore candidate trays image data for the X-ray scanner system thatcomprises characteristic image quality data collected from the one ormore candidate trays that have been scanned with a same plurality ofselected tray inserts; extracting image quality metrics for both thebaseline image data and the one or more candidate trays image data;performing statistical analysis using the image quality metrics within avolume of interest from both the baseline image data and the candidatetrays image data; and reporting or certifying the one or more candidatetray suitable for use in the X-ray scanner system based on the imagequality metrics of the one or more candidate tray falling within a meanand a standard deviation of CTN values within the volume of interest. 2.The computer implemented method of claim 1, wherein the collecting ofthe baseline image data or the one or more candidate trays image data,comprising: taking turns to scan a defined number of times by the X-rayscanner system, one of the same plurality of selected tray inserts whichhas been centrally positioned in the plurality of certified trays or theone or more candidate trays, until the remaining selected tray insertshave been scanned with the plurality of certified trays or the one ormore candidate trays by the X-ray scanner system.
 3. The computerimplemented method of claim 1, wherein the extracting of the imagequality metrics for both the baseline image data and the candidate traysimage data, comprising: calculating a “z-slice” or a cross-sectionimage, of the selected tray insert which has been positioned withineither the plurality of certified trays or the one or more candidatetrays, in the xy-plane at a specific z-location, and constructing athree-dimensional (3D) volumetric image data represented by athree-dimensional matrix I(x,y,z), based on projections of thethree-dimensional volumetric image data onto a single plane.
 4. Thecomputer implemented method of claim 1, wherein the performing ofstatistical analysis using the image quality metrics within a volume ofinterest from both the baseline image data and the candidate trays imagedata, comprising: processing all z-slices in the volume of interest tocalculate a final mean and standard deviation metrics, wherein eachz-slice within the volume of interest containing 1‘s for all pixelswithin the region of interest and 0's for all pixels outside, and anumber of non-zero pixels remaining in each z-slice (N_(non-zero, z))are counted and summed to obtain CTN and CTN² values for all the pixelsto yield running totals to calculate the final mean and standarddeviation metrics:$\overset{\_}{CTN} = {{CTN}_{total}/N_{{counts}{total}}}$$\sigma_{CTN} = {\sqrt{{{CTN}_{total}^{2}/N_{{counts}{total}}} - {\overset{\_}{CTN}}^{2}}.}$5. The computer implemented method of claim 1, comprising prior to thecollecting of the baseline image data for the X-ray scanner system,preparing the X-ray scanner system for data collection accuracy bymeasuring physical dimensions of both the plurality of certified traysor the one or more candidate trays to meet tray size requirements. 6.The computer implemented method of claim 1, wherein the tray insertcomprises: a body, wherein the body having multiple parts removablypositioned and prearranged therein for generation of image qualitymetrics for tray impact evaluation in an X-ray system; a first coverdisposed at a first end of the body; and a second cover disposed at asecond end of the body, wherein the first cover and the second cover areconfigured to fix and secure the body at the both ends, wherein themultiple parts are configured for screening presence of explosivethreats by the X-ray scanner system that utilizes screening technologycomprising one of: two-dimensional (2-D) X-ray or X-ray computertomography (CT).
 7. The computer implemented method of claim 2,comprising orientating a front end of the selected tray insert to alignwith a same direction of belt movement of the X-ray scanner system. 8.The computer implemented method of claim 7, comprising rejecting the oneor more candidate trays when the 3D volumetric image data exhibitspresence of X-ray artifacts.